In order to produce magnetic resonance tomography images of different phases of a movement of an object, such as of the masticatory movement of a jaw, images are frequently taken of individual positions of the movement. For this purpose, a device for fixing the object, such as the jaw, in the various positions is required. As a result of the fixing, sufficient magnetic resonance tomography data for an image can be produced for each individual position. However, this manner of taking images is very complex and possible dynamic effects, which only occur during a real motion sequence, cannot be represented.
If the movement is sufficiently slow, for example, if the masticatory movement is performed slowly enough, it is also possible to produce sufficient magnetic resonance tomography data for each movement phase during a single movement cycle. This is described for example in “Real-Time Magnetic Resonance Imaging of Temporomandibular Joint Dynamics,” S. Zhang et al., published in The Open Medical Imaging Journal, 5, 1-7, 2011.
However, the slow performance of a masticatory movement, for example, is very challenging for a patient and must be practiced or assisted. In addition, dynamic effects that only occur at the normal speed of movement cannot be represented with this method.
In a cyclic movement, it is furthermore possible to generate data sets over several cycles of the movement and subsequently to assign a phase of the movement in each case to the data sets. In this way, all data sets corresponding to the phase can be taken as the basis for an image of the corresponding movement phase.
To make assignment possible, the positions of the movement can, for example, be recorded using a device. However, the costs resulting from the additional device are disadvantageous.
From “Adaptive Averaging Applied to Dynamic Imaging of the Soft Palate” by A. D. Scott et al., published in Magnetic Resonance in Medicine, volume 70, pages 865-874, September 2013, a method for producing recordings of movements is known, which determines correlation coefficients between individual real-time images.
Based on the correlation coefficients, real-time images of the same movement phase are identified and taken as the basis for an overall image of this movement phase.
The method described thus correlates images with a low signal-to-noise ratio (SNR), without a cyclic movement having to be present for this purpose. The images with a high correlation coefficient are averaged in order to increase the SNR. The method does however have the disadvantage that the temporal and spatial resolution is limited due to the dense scanning of a k-space required to obtain artifact-free images. The spatial resolution is, for example, between 1.6×1.6 and 2.0×2.0 mm^2. The temporal resolution can be 50-111 ms, for example, due to parallel imaging with several coils. For measurements with one coil, the temporal resolution would only be 150-300 ms. The required spatial and temporal resolutions for TMJ (temporomandibular joint dysfunction) recordings are at most 0.75×0.75 m^2 and 100 ms respectively.
Another disadvantage is that incorrect or ambiguous maxima of the pairwise correlation can lead to distorted results and this reduces the robustness of the method.